Systems and Methods for Direction Finding Using Augmented Spatial Sample Covariance Matrices

ABSTRACT

In an array antenna having a plurality of subarrays, a direction finding system and technique includes receiving signals at an array antenna and capturing data with a plurality of groups of subarrays. Each group of subarrays may capture data during a selected one of a plurality of different dwell times. The method further includes generating a plurality of dwell spatial sample covariance matrices (SCMs) using data corresponding to one or more of the plurality of groups of subarrays and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix (ACM). The ACM may then be used in subsequent processing with MINDIST technique to estimate a direction of a received signal based on the combined data.

GOVERNMENT INTERESTS

This invention was made with the government support. The government hascertain rights in this invention.

BACKGROUND

As is known in the art, direction finding (DF) may be described as adetermination of a direction from which a received radio frequency (RF)signal was transmitted. To make such a determination, a DF systemreceives RF signals at one or more antenna elements and processes thesignals in a receiver. Increasing the number of antenna elements whichreceive the RF signal and providing each received signal to a receiverchannel for further processing can increase the accuracy of theestimate. Many receive systems, however, have a limited number ofreceiver channels with which to simultaneously receive and process thesamples from multiple antenna elements.

SUMMARY

The concepts, systems and methods described herein are directed towardsdirection finding (DF) techniques using a switched network architectureto couple a first plurality of antenna elements to a second, fewer,plurality of channels of a radio frequency (RF) receiver. The RFreceiver provides appropriately processed signals to a DF processorwhich combines data sampled at phase centers of the plurality of arrayelements. Such combined data samples may then be used to estimate adirection of a received signal. In some embodiments, the antennaelements are configured into subarrays and a switch network couplesdifferent groups of subarrays to the RF receiver channels duringdifferent dwell times. Data collected during each dwell used to generatea spatial sample covariance matrix (SCM) and multiple spatial SCMs onecombined to provide an aggregate covariance matrix values in theaggregate covariance matrix are then used to estimate a direction of areceived signal.

In accordance with the concepts described herein a DF system includes, afirst plurality of array antenna elements is coupled to a second,different plurality of RF receiver channels through a switch network. Insome embodiments, the number of array antenna elements may be greaterthan the number of receiver channels and during a dwell time the switchnetwork is configured to select a number of antenna elements equal to anumber of receiver channels such that signals are coupled from aselected number of antenna elements equal to an equal number of receiverchannels. This approach allows data to be collected from phase centersof a plurality of array elements and be substantially simultaneouslyprovided to individual receiver channels of the RF. This is repeated fordifferent pluralities of antenna elements during different dwell times.

In some embodiments, the antenna elements are formed into subarrays. Inresponse to control signals provided thereto, the switch networkswitches between different ones of a plurality of subarrays of an arrayantenna so as to gather data from different subarrays during differentdwell times.

Each subarray may comprise one or more antenna elements. Significantly,during a first dwell time the switch network simultaneously couples aselected combination or group of subarrays to a set of receiver channelswhere the number of subarrays equals the number of receiver channels.During a next dwell time the switch matrix couples a differentcombination (or group) of subarrays to the receiver channels. Thisprocess is repeated for each of a plurality of dwell times and a likeplurality of groups of subarrays. Thus, the receiver generates datasamples from a plurality of different groups of subarrays at a likeplurality of different dwell times. The data may be collectedsubstantial at the phase centers of each subarray although it should beappreciated that each subarray need not have the same phase center.

The data collected during each dwell is used to generate a complexspatial SCM. Each of the so-generated dwell SCM's can be combined toform an aggregate covariance matrix. Complex angle information from theaggregate covariance matrix can be extracted and provided to a DFprocessor which uses values from the aggregate covariance matrix toprovide an output signal indicative of the direction of the receivedsignal.

In an embodiment, the DF processor uses a MINDIST technique to providean accurate determination of a direction of arrival of RF signalsincident on an antenna having arbitrarily located antenna phase centersin a computationally efficient manner. In an embodiment, the MINDISTtechnique compares elements (i.e., matrix elements) of a reduceddimension spatial SCM to principal component vectors for a given phasecenter location as a function of antenna beam angle and frequency. Theprincipal component vectors may be stored in one or more tables andgrouped or otherwise organized as a function of phase center location,antenna beam angle and/or frequency.

In an embodiment, the SCM elements from the aggregate covariance matrixhaving a non-zero angle (i.e., an angle that is greater than or lessthan zero) may be identified and a portion of such non-zero elements maybe used to generate principal components for a principal component table(or p-table).

By eliminating components of the SCM having a value of zero andrecognizing a symmetric nature of the SCM less than one-half of the SCMelements are required for computations. By extracting one-half of theSCM elements and using them in later computations, a total computationtime of the MINDIST method may be reduced. For each of the principalcomponents, vector data may be computed and stored (e.g., in tables). Insome embodiments, the vector data may be precomputed. Thus, for each ofa plurality of extracted principal components, the corresponding vectordata may be identified and used to generate the p-table. The p-tablethus has the principal components values stored therein. In someembodiments, the extracted principal component values may be sorted byfrequency and angle data.

In operation, a test point can be compared to each of the entries in thep-table to identify a minimum distance point. The MINDIST method maycompare distance measurements to precomputed tables making the methodsherein computationally feasible. The minimum distance point correspondsto a direction of arrival of the RF plane waves incident on the arrayelements. Thus, the MINDIST technique provides an estimation of an angleof arrival of RF signals.

In one aspect, in an array antenna having a plurality of subarrays, amethod for direction finding comprises receiving signals at an arrayantenna and capturing data with a plurality of groups of subarrays. Inan embodiment, each group of subarrays may capture data during aselected one of a plurality of different dwell times. The method furthercomprises generating a plurality of dwell spatial SCMs using datacorresponding to one or more of the plurality of groups of subarrays andcombining the plurality of dwell spatial SCMs in complex form togenerate an aggregate covariance matrix.

In an embodiment, the method further comprises pre-computing a principalcomponent table using angle and frequency measurement for one or moreprincipal components, extracting the one or more principal components ascomplex phases from the aggregate covariance matrix to form a testpoint, determining a distance between the test point and each value inthe pre-computed principal component table and identifying a minimumdistance point based on the determined distances between the test pointand each value in the principal component table, wherein the minimumdistance point corresponds to a direction of the received signals.

In some embodiments, the data may be captured at a plurality of stages,wherein each stage includes two or more switches. For example, data maybe received at a first stage from at least one subarray and the data maybe provided to two or more switches in a second stage of switches. Thedata may be received at the second stage of switches from the firststage of switches and the data may be provided from a portion of the atleast one subarray to a direction finding module.

In an embodiment, the method comprises generating one or more dwellsusing the data from the plurality of groups of subarrays, wherein eachdwell corresponds to at least one dwell time of the plurality ofdifferent dwell times. In some embodiments, the method comprisescapturing the data at a first subarray at a first dwell time, capturingthe data at a second subarray at a second dwell time, generating a dwellspatial SCM for each of the first subarray and the second subarray andcombining the dwell spatial SCMs in complex form to form the aggregatecovariance matrix.

In an embodiment, the method comprises providing a first group data froma first group of subarrays to a switch matrix and providing a secondgroup of data from a second group of subarrays directly to a directionfinding module. The data may include angle measurements corresponding tothe received data relative to a phase center of one or more of theplurality of array elements. In some embodiments, each of the dwell SCMsand the aggregate covariance matrix may include angle measurements forthe plurality of array elements. The method may comprise identifying aphase difference between each of the elements in the aggregatecovariance matrix using the angle measurements.

In an embodiment, the method comprises determining vector data for eachof the plurality of array elements. The vector data may include angleand frequency measurements. In some embodiments, the principal componenttable may include principal component data sorted by frequencymeasurements and angle measurements for each of the one or moreprincipal components.

In another aspect, a system for direction finding is provided comprisesa plurality of array elements to receive signals, a direction findingmodule and one or more receiver channels to couple the plurality ofarray elements to the direction finding module. In an embodiment, thenumber of array elements may be greater than the number receiverchannels. The one or more receiver channels comprise a switch matrixdisposed in a signal path between the plurality of array elements andthe direction finding module to switch between different groups ofsubarrays of the plurality of array elements and collect data for atleast one group of subarrays during a selected one of a plurality ofdwell times and provide the data to the direction finding module.

In an embodiment, at least one array element in each of the groups ofsubarrays may be active and at least one array element in each of thegroups of subarrays may be inactive. The switch matrix may comprise aplurality of stages and each of the plurality of stages may include twoor more switches. In some embodiments, a first stage of switches may becoupled to each of the plurality of array elements to receive the datafrom at least one group of subarrays and may be configured to providethe data to two or more switches in a second stage of switches. Thesecond stage of switches may be coupled to the first stage of switchesto receive the data from the first stage of switches and may beconfigured to provide the data from a portion of the at least one groupof subarrays to the direction finding module.

In an embodiment, the plurality of array elements may comprise a firstgroup of array elements coupled to the switch matrix and a second groupof array elements coupled directly to the direction finding module.

In an embodiment, the direction finding module comprises a spatial SCMmodule to receive the data and generate one or more dwell spatial SCMsand an aggregate covariance matrix using the data, a p-table module togenerate a table having components as a function of frequency and anglemeasurements, and a principal component module coupled to the SCM moduleand the p-table module. The principal component module may generate aprincipal component table having one or more principal components sortedby the frequency and angle measurements. The direction finding modulefurther comprises a distance measurement module coupled to the principalcomponent module. The distance measurement module may calculate adistance from a test point to each entry in the principal componenttable and a minimum distance module coupled to the distance measurementmodule. The minimum distance measurement module may determine a minimumdistance point based on the calculated distances from the test point toeach entry in the principal component table.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing concepts and features may be more fully understood fromthe following description of the drawings. The drawings aid inexplaining and understanding the disclosed technology. Since it is oftenimpractical or impossible to illustrate and describe every possibleembodiment, the provided figures depict one or more illustrativeembodiments. Accordingly, the figures are not intended to limit thescope of the concepts, systems and techniques described herein. Likenumbers in the figures denote like elements.

FIG. 1 is a block diagram of a direction finding (DF) system havingsubarrays coupled to individual receiver channels through a switchnetwork;

FIGS. 2A-2C are illustrations of different combinations of subarraysbeing used during different dwell times of the DF system of FIG. 1;

FIG. 3A is block diagram of a DF system having a switch network disposedbetween a plurality of array elements;

FIG. 3B is block diagram of a DF system having one or more subarraysdirectly coupled to receiver channels and a plurality of subarrayscoupled to a few number of receiver channels through a switch network;

FIGS. 4A and 4B are flow diagrams of a method for performing directionfinding using data from a plurality of different subarrays gathered atdifferent dwell times; and

FIG. 5 is a block diagram of an embodiment of a processing system forperforming DF processing in accordance with the techniques describedherein.

DETAILED DESCRIPTION

Now referring to FIG. 1, a direction finding (DF) system 100 includes anarray antenna 101 having a plurality of subarrays 102 a-102T. Each ofthe subarrays 102 a-102T may comprise one or more individual antennaelements (also referred to as “elements” or “radiators”). Thus, eachsubarray 102 a-102T may represent a single antenna element or multipleantenna elements.

For reasons which will become apparent from the description hereinbelow, during each of multiple dwell times, a switch network (alsosometimes referred to herein as a switch matrix) 110 couples selectedones of a first plurality of subarrays (here, selected ones of subarrays102 a-102N) to a second, fewer plurality of receiver channels (here,receiver channels 108 a-108M) of a receiver 107. One or more othersubarrays (e.g. subarrays 102 p-102T) may be coupled directly toindividual receiver channels (e.g. receiver channel 108R) of receiver107. Receiver 107 processes the signals provided thereto as is generallyknown. Receiver channels 108 a-108R may operate, for example, to amplifyand/or down convert and/or demodulate the signals provided thereto.

Outputs of receiver 107 are coupled to a DF processor 130. DF processor130 receives the signals provided thereto and processes the signals togenerate an estimate of a direction of arrival (i.e. angle of arrival)of a radio frequency (RF) plane wave incident on array antenna 101. DFprocessor 130 may, for example, utilize a minimum distance (MINDIST)technique to produce a DF output signal indicative of a direction fromwhich the received signal emanated. The MINDIST technique is describedin co-pending application Ser. No. 15/260,508, entitled “Systems AndMethods For Direction Finding Based On Minimum Distance Search ToPrincipal Components,” filed on the same date herewith, assigned to theassignee of the present application, and hereby incorporated herein byreference in its entirety.

As noted above, in an embodiment, each subarray 102 a-102T may compriseone or more antenna elements. In some embodiments, each subarray mayinclude two or more array elements. In one embodiment, each subarray mayinclude a single element. In some embodiments, each subarray may includethe same number of elements. In other embodiments, different subarraysmay include a different number of elements.

A number of array elements to include in a particular subarray may beselected based, at least in part, upon a total number of elements inarray antenna 101, a number of individual receiver channels included inreceiver 107, a desired gain pattern of DF system 100, and/or a field ofview (FoV) of DF system 100. It should also be appreciated that any typeof antenna element may be used to implement array antenna 101.

It should also be appreciated that switch matrix 110 may be providedfrom any of a variety of different types of switches includingmulti-pole switches or multi-throw switches. A particular type of switchused in switch matrix 110 may depend at least in part on a particularapplication of the DF system 100 and the components of the DF system 100(e.g., number of array elements, number of receiver channels).

Further, it should be appreciated that in the illustrative embodiment ofFIG. 1, some of the subarrays (i.e. subarrays 102 a-102N) are coupled toreceiver channels through switch matrix 110 while other subarrays (e.g.subarrays 102 p-102T) are coupled directly to individual channels ofreceiver 107. As will be described below in conjunction with FIG. 3A, insome embodiments, all subarrays may be coupled to receiver channelsthrough a switch network. It should be appreciated that the particularnumber of subarrays to couple to receiver channels through a switch(e.g. switch matrix 110) may be selected based upon a particularapplication of DF system 100 and/or a number of subarrays required fordesired system operation as compared with a number of receiver channels108 a-108R.

Now referring to FIGS. 2A-2C, in which like elements are provided havinglike reference designations, an array antenna 200 utilizes selected onesof five subarrays 204 a-204 e in different combinations (or groups) toperform direction finding. It should be appreciated that in thisillustrative embodiment, it is assumed that antenna 200 provides signalsto a four channel receiver. Thus, it is not possible to simultaneouslyconnect all five subarrays to a unique receiver channel. Thus, asillustrated in FIG. 2A, during a first dwell time 202 a, a first group(or combination) of subarrays, here four subarrays 204 a-204 d, areactive to receive signals. A fifth one of the subarrays (here subarray204 e) is made inactive. Thus, during the first dwell time 202 a RFsignals received via a first group of subarrays (i.e. subarrays 204a-204 d) are coupled through a switch (e.g. switch matrix 110 describedabove in FIG. 1) to individual receiver channels (e.g. receiver channels108 a, 108 b, 108 c, 108 d in FIG. 1) such that signals from each of theselected subarrays 204 a-204 d may be simultaneously processed (e.g. ina DF processor such as DF processor 130 described above in FIG. 1) togenerate a first dwell spatial sample covariance matrix (SCM) 205 a.

Similarly, and referring to FIG. 2B, during a second dwell time 202 b, asecond different group (or combination) of subarrays (here subarrays 204a, 204 b, 204 d, 204 e) are selected to receive signals. A fifth one ofthe subarrays (here subarray 204 c) is inactive. Thus, during the seconddwell 202 b, RF signals received via the second group of subarrays (i.e.subarrays 204 a, 204 b, 204 d, 204 e) are coupled through a switch toindividual receiver channels such that signals from each of the selectedsubarrays may be simultaneously processed to provide a second dwellspatial SCM 205 b.

Similarly, and referring to FIG. 2C, during a third dwell time 202 c, athird, different group (or combination) of subarrays (here subarrays 204a, 204 b, 204 c, 204 e) are selected to receive signals. A fifth one ofthe subarrays (here subarray 204 d) is inactive. Thus, during a thirddwell 202 c, RF signals received via the third group of subarrays (i.e.subarrays 204 a, 204 b, 204 c, 204 e) are coupled through a switch toindividual ones of four receiver channels such that signals from each ofthe selected subarrays may be simultaneously processed to provide athird spatial SCM 205 c.

With this arrangement, data is captured during a plurality of differentdwells 202 a-202 c using three different groups (or combinations) ofsubarrays, i.e. a first group comprising subarrays 204 a-204 d; a secondgroup comprising subarrays 204 a, 204 b, 204 d and 204 e; and a thirdgroup comprising subarrays 204 a-204 c and 204 e. Thus, data is providedfrom a specific group of subarrays at specific dwell times. Stateddifferently, during each of the first, second and third dwells 202 a-202c signals are received with different combinations (or groups) ofsubarrays 204 a-204 e. Thus, the data sampled from the different groupsof subarrays 204 a-204 e are sampled at different periods of time.

Although FIGS. 2A-2C illustrate only three dwells and three groups ofsubarrays, it should be appreciated that any number of subarrays and anynumber of dwells may be used. The number of dwells to use in aparticular application is based, at least in part, on the number ofsubarrays and/or receiver channels in a DF system.

As illustrated in FIGS. 2A-2C, during each of the different dwells 202a-202 c, at least one of the subarrays is inactive. An inactive subarraymay refer to a subarray which is configured or otherwise coupled to notreceive any signals or may refer to a subarray having an output notcoupled to a receiver channel. As will be described in conjunction withFIG. 3B this may be accomplished, for example, by coupling a subarray toa matched termination.

Once the data is collected, the first, second and third dwell spatialSCMs 205 a-205 c can be combined in complex form to generate anaggregate covariance matrix (AGM). The dwell SCMs and dwell AGM will bedescribed in greater detail below in conjunction with FIGS. 3-4B.

Now referring to FIG. 3A, a DF system 300 includes an array antenna 301comprising a plurality of subarrays 302 a-302N coupled through a switchmatrix 310 to a second different plurality of RF receiver channels 308a-308M of an RF receiver 307. Outputs of receiver channels 308 a-308Mare coupled to inputs of a DF processor 330. In the illustrativeembodiments of FIG. 3A, DF processor 330 implements a MINDIST DFtechnique. Array 301, subarrays 302 a-203N, switch matrix 310 andreceiver channels 308 a-308M may be the same as or similar to subarrays102 a-102T, switch matrix 110 and receiver 107 described above inconjunction with FIG. 1 and array 200 of FIGS. 2A-2C.

In some embodiments, the plurality of array elements 302 a-302N, switchmatrix 310 and the plurality of receiver channels 308 a-308M may beseparate from DF processor 330. In other embodiments, the plurality ofarray elements 302 a-302N, switch matrix 310 and the plurality ofreceiver channels 308 a-308M may be integrated within DF processor 330.

It should be appreciated that in this illustrative embodiment, allsubarrays 302 a-302N are coupled to receiver channels 308 a-308M throughthe switch matrix 310.

In an embodiment, the number of subarrays 302 a-302N may be greater thanthe number of receiver channels 308 a-308M (i.e. N>M). Thus, switchmatrix 310 may be operated to couple selected ones (or groups orcombinations) of subarrays 302 a-302N to individual receiver channels308 a-308M.

A group of M subarrays may be designated for use at a specific dwelltime. Thus, for each dwell time, samples of data from the pre-selectedthe group of M subarrays may be coupled through the switch matrix 310 tothe M receiver channels 308 a-308M and subsequently to DF processor 330.

The particular subarrays used in each group of subarrays from whichsignals are received may change from one dwell time to a next dwelltime. Also, in some embodiments, each group of subarrays may include oneor more active and one or more inactive array elements. In someembodiments, to form different groups of subarrays, the array elementsthat are active versus inactive may change from one dwell time to a nextdwell time.

For example, in an embodiment, during a first dwell time switch matrix310 may operate to couple a subarray to reference potential (e.g.,ground) through a matched load such that the subarray, terminates,rejects or ignores signals provided thereto during a first dwell time.Thus, the respective subarray may be considered to be inactive as nodata is being sampled from the respective subarray. During a seconddwell time, however, the same respective array element may be consideredactive as data is being sampled from the respective array element. In anembodiment, the number of active subarrays is selected based upon thenumber of array elements, a number of receiver channels.

In an embodiment, signals received via subarrays 302 a-302N may besamples of signals incident on the respective array element at specificpoints in time (e.g., a snapshot at a particular dwell time). Thesamples of data may be taken at or relative to a phase center ofrespective ones of array elements 302 a-302N.

The data may be expressed as complex values (e.g., I/Q data)representing the signal. For example, in some embodiments, the data maycorrespond to voltage signals represented as complex values representingangle of arrival, amplitude, phase, and/or a polarization of the signal,for example. In an embodiment, the data may correspond to a value of asignal at a predetermined point in time (e.g., a snapshot at aparticular dwell time) or over a predetermined time period. The datacorresponding to the plurality of signals may be coupled or otherwiseprovided to an input of the DF processor 330.

In the illustrative embodiment of FIG. 3A, the DF processor 330 includesa spatial SCM module 335, a principal components module 340, a distancemeasurement module 350, a minimum distance module 355 and a p-tablemodule 345.

The SCM module 335 receives data provided thereto from receiver 307 andgenerates a matrix of values (i.e., an SCM) using the data (which may besamples of data). For example, SCM module 335 may generate a pluralityof dwell SCMs 336 a-336N. Each dwell SCM 336 a-336N can be associatedwith one or more receiver channels 308 a-308M. In some embodiments, eachdwell SCM 336 a-336N can be associated with a respective receiverchannel 308 a-308M. In other embodiments, each dwell SCM 336 a-336N canbe associated with multiple receiver channels 308 a-308M. Each dwell SCM336 a-336N may be based, at least in part, on data sampled from aselected group of subarrays 302 a-302N at specific dwell time (e.g. oneof dwells 202 a-202 c in FIGS. 2A-2C) and provided by the switch matrix310 to a receiver channel 308 a-308M associated with the selected groupof subarrays 302 a-302N at that specific dwell time. Examples of dwellSCM's generated using dwells such as those shown in FIGS. 2A-2C areprovided below in Tables 1-3.

Table 1 below is an illustration of complex data values of the type ofwhich may be sampled during a first dwell (e.g. dwell 202 a of FIG. 2A)by a first group of subarrays (e.g. subarrays 204 a-204 d in FIG. 2A).

TABLE 1 27.1313 −25.3736 − 1306061 − 25.2114 + 0 j6.0954 j20.2535j5.9324 −25.3734 + 26.879 −8.4809 + −25.8083 + 0 j6.0954 j22.7156j0.1241 13.6061 + −8.4809 − 23.5265 8.3668 + 0 j20.2535 j22.7156j22.5207 25.2114 − −25.8083 − 8.3668 − 26.5831 0 j5.9324 j0.1241j22.5207 0 0 0 0 0

It should be noted that the data sampled for a first dwell is expressedin complex form thereby preserving both amplitude and phase of thereceived signal from which the matrix values are generated. The entrieshaving a “0” value represent an inactive subarray (i.e. a subarray withwhich data was not sampled and thus not provided to the DF processor330). For example, and briefly referring to FIG. 2A, Table 1 mayrepresent data from a group of subarrays 204 a-204 e with four subarrays204 a-204 d being active and one subarray 204 e being inactive. Thus,the fifth row and fifth column of the matrix corresponds to the inactivefifth subarray 204 e and thus, the matrix values in the fifth row arezero.

Table 2 below is an illustration of complex data values of the typewhich may be sampled for a second dwell (e.g. dwell 202 b of FIG. 2B).

TABLE 2 27.3245 −25.4644 − 0 25.2159 + −9.9436 + j6.0953 j5.9324j24.1408 −25.4644 − 26.9083 0 −25.6667 + 4.0905 − j6.0953 j0.1241j25.6354 0 0 0 0 0 25.2159 − −25.6667 − 0 26.348 −3.9979 + j5.9324j0.1241 j25.4416 −9.9436 − 4.0905 + 0 −3.9979 − 27.0664 j24.1408j25.6354 j25.4416

The data captured during the second dwell is also expressed in complexform. Briefly referring to FIG. 2B, Table 2 is an illustration of datawhich may be provided from a selected group of active subarrays (e.g.subarrays 204 a, 204 b, 204 d, 204 e and an inactive subarray (i.e.subarray 204 c). Thus, the matrix values in the third row and column ofthe matrix corresponding to inactive subarray 204 c are zero.

Table 3 below represents complex data values captured during a thirddwell (e.g. dwell 202 c of FIG. 2C).

TABLE 3 27.2336 −25.4096 − 13.6792 − 0 −9.9754 + j6.0950 j20.2522j24.1393 −25.4096 + 26.8858 −8.4945 + 0 3.9047 − j6.0950 j22.7141j25.6338 13.6792 + −8.4945 − 23.6675 0 −23.6508 + j20.2522 j22.7141j4.7847 0 0 0 0 0 −9.9754 − 3.9047 + −23.6508 − 0 27.0452 j24.1393j25.6338 j4.7847

In an embodiment, the data sampled for third dwell 202 c of FIG. 2C isin complex form representing the signal using real and imaginary parts.

Briefly referring to FIG. 2C, Table 3 may represent data from a group offive subarrays 204 a-204 e. Of the five subarrays, the first, second,third and fifth subarrays 204 a, 204 b, 204 c, 204 e may be active andthe fourth subarray 204 d may be inactive. Thus, the fourth row andcolumn of data corresponding to the inactive fourth subarray 204 d havevalues of zero.

In an embodiment, the SCM module 335 may combine each of the dwell SCMsto generate an aggregate covariance matrix. The dwell SCMs may becombined in complex form (e.g., quadrature space), thus the aggregatecovariance matrix also includes complex data. For example, an exampleaggregate covariance matrix is provided below in Table 4.

TABLE 4 81.6894 −76.2475 − 27.2853 − 50.4272 + −19.9189 + j18.2857j40.5056 j11.8648 j48.2801 −76.2475 + 80.6731 −16.9755 + −51.4751 +7.9952 − j18.2857 j45.4297 j0.2482 j51.2692 27.2853 + −16.9755 − 47.19418.3668 + −23.6508 + j40.5056 j45.4297 j22.5207 j4.7847 50.4272 −−51.4751 − 8.3668 − 52.9312 −3.9979 + j11.8648 j0.2482 j22.5207 j25.4416−19.9189 − 7.9952 + −23.6508 − −3.9979 − 54.1116 j48.2801 j51.2692j4.7847 j25.4416

In an embodiment, the aggregate covariance matrix of Table 4 is acombination of the combined values of the dwell SCMs of Tables 1-3. TheSCM module 335 may generate an angle SCM that identifies an angle ofeach of the SCM entries in the aggregate covariance matrix. For example,using the real and imaginary parts of the complex number in each entryof the aggregate covariance matrix, the SCM module 335 may calculate acomplex angle for each entry of the aggregate covariance matrix. Thecomplex angles from each of the entries may be used to form the angleSCM. For example, an example angle SCM is provided below in Table 5.

TABLE 5 0 −2.9062 −0.978 0.2311 1.9621 2.9062 0 −1.9284 3.1368 −1.41610.978 −1.9284 0 1.2151 2.942 −0.2311 −3.1368 −1.2151 0 1.7267 −1.96217.9952 + −2.942 −1.7267 0 j51.2692

In an embodiment, Table 5 represents an angle SCM generated using thedata from aggregate covariance matrix of Table 4 and thus, using thedata from the first, second and third dwells 202 a-202 c of FIGS. 2A-2C.The angles in Table 5 are represented in terms of radians. In anembodiment, by combining data from a plurality of groups of subarrayscorresponding to a plurality of array elements, an accurate angle SCMcan be generated that is the same as or substantially similar to anangle SCM generated using data from each of the plurality of elements ata single dwell time.

The SCM module 335 may provide the angle SCM to an input of theprincipal components module 340. In some embodiments, the SCM module 335may only provide a portion of the entries of the angle SCM (e.g.,so-called principal components as will be explained in detail furtherbelow) to the principal components module 340.

The principal components module 340 may extract so-called principalcomponents from the angle SCM. In an embodiment, the principalcomponents may correspond to the SCM entries having a non-zero angle. Anon-zero angle refers to an angle that is greater than or less than 0(thus not equal to zero). For example, the angle SCM may include SCMentries that compare data taken at array elements to themselves (e.g.,Δφxx=0). Thus, the respective angle for these entries may equal zero.

Referring back to Table 5 above, the values along the main diagonal ofthe angle SCM represent the difference between two samples of data takenat the same array element. Hence, the values of the main diagonal areideally equal to zero. In some embodiments, the SCM entries having anangle equal to zero may be removed from further processing or ignored.Thus, SCM entries having a non-zero angle may be extracted from the SCM.

In some embodiments, the principal components module 340 may extractonly a portion of the non-zero angle SCM entries. For example, the angleSCM may include SCM entries that compare the two different arrayelements (i.e. array element M and array element N) to generate a firstSCM entry Δφmn and a second SCM entry Δφnm. In this case, it has beenrecognized that the first SCM entry will be equal in amplitude to thesecond SCM entry (e.g., Δφmn=Δφnm). For example, and referring to table5 above, the value in the first column, second row may correspond to thevalue in the second column, first row. Thus, only one of these twovalues, one of the first and second SCM entries, may be needed forprocessing.

In one embodiment, the principal components module 340 extracts one-halfof the angle SCM entries having a non-zero angle. The principalcomponents module 340 may generate a non-zero SCM entry table comprisingthe extracted angle SCM entries having a non-zero angle.

The p-table module 345 may generate one or more tables having storedtherein principle component values (p-table values). The p-table valuesmay be stored and indexed for example, as a function of angle andfrequency. In an embodiment, the angle corresponds to an angle ofarrival of a received signal incident on one or more of the plurality ofarray elements 302 a-302 n. In some embodiments, the angles may besorted two-dimensional angles (e.g., azimuth angle, elevation angle).

In an embodiment, p-table may be provided as a precomputed table (e.g.,a table having precomputed values stored therein) or a principalcomponent table (e.g., a table having principal components based onmeasured data). The precomputed p-table may be generated using the samemethods as described above, however, the pre-computed p-table may begenerated using previous data measurements and/or estimates. Forexample, the p-table may be precomputed using previously collectedsnapshots at one or more of the plurality of array elements 302 a-302N.The data from the previous snapshots may be used to generate apre-computed angle SCM). Using the pre-computed angle SCM, principalcomponents may be identified and extracted from the SCM. Thus, theprecomputed p-table may be generated using the extracted principalcomponents based on the data from the previous snapshots.

In other embodiment, the p-table may be precomputed using estimatedarray properties (e.g., array manifold vector properties). For example,estimated array properties may be used to generate estimated snapshotsfor one or more of the plurality of array elements 302 a-302N. Thepre-computed p-table may be generated using the methods as describedabove based on the estimated data (e.g., snapshots) for the arrayelements 302 a-302N.

In an embodiment, the frequency and angle components may be precomputedbased upon estimations or previously collected data. For example, insome embodiments, the frequency and angle components may be pre-computedusing previously collected snapshots. In other embodiments, thefrequency and angle components may be precomputed using estimatesperformed based on known phase centers for one or more of the pluralityof array 302 a-302N. In some embodiments, using estimated or measuredphase center locations, the frequency and angle components may beprecomputed using an array manifold vector corresponding to theplurality of array elements 302 a-302N.

For example, for a desired range, the p-table module 345 may perform astatistical analysis for measuring the amount of power or informationcontained in the data stored in the p-table. Such an analysis may beperformed for all angles within a desired range relative to apredetermined azimuth range and a predetermined elevation range. In anembodiment, a principal component analysis may be used to measure theamount of power and/or information contained in multivariate data, herethe desired range relative to the predetermined azimuth range and thepredetermined elevation range. For example, the principal componentanalysis may be performed for each of the array elements over a desiredfrequency range using an array manifold vector corresponding to theplurality of array elements 302 a-302N. Thus, the p-table module 345 maygenerate vector data (e.g., principal component data) for each of theplurality of array elements 302 a-302N. In some embodiments, the vectordata may be generated (estimated) based on the phase center locations ofthe plurality of array elements 302 a-302N.

In some embodiments, the p-table module 345 multiplies the resultingvector data for each array element by its complex conjugate and/or thecomplex conjugate of vector data from another array element and storesthe result in a vector data table.

In an embodiment, the p-table module 345 compares the vector data tableto an SCM phase difference matrix to identify the vector datacorresponding to the angle SCM entries having a non-zero angle andextract the corresponding vector data. The p-table module 345 generatesa final p-table that sorts each of the SCM entries having a non-zeroangle by their respective vector data (e.g., by frequency and angledata). In an embodiment, the final p-table may be a principal componenttable. Thus, the final p-table may include the extracted principalcomponent data sorted or otherwise grouped or arranged by frequency andangle.

In some embodiments, the p-table module 345 may generate one or morep-tables prior to the DF processor 330 receiving signals or data fromthe plurality of array elements 302 a-302N. In other embodiments, thep-table module 345 may generate one or more p-tables simultaneously(e.g., real-time) to the DF processor 330 receiving signals or data fromthe plurality of array elements 302 a-302N.

In some embodiments, the number of p-tables generated may vary and/orthe size or number of elements in a p-table may vary according to aparticular application of the MINDIST method. For example, in someembodiments, the p-table may be generated for a desired angular field ofview (e.g., ±M° az, ±N° el). Thus, the number of elements in the p-tableis related at least to the desired angular field of view.

The distance measurement module 350 receives the principal componentvalues from the principal components module 340 and receives a p-tableprovided by the p-table module 345. The distance measurement module 350calculates a distance between a test point and each of the entries inthe p-table. In an embodiment, the test point may refer to a data pointmeasured in real time. For example, the test point may be formed byextracting principal components as complex phases from the aggregatecovariance matrix or the angle SCM generated by SCM module 335. Thus,the test point may correspond to data currently received from arrayelements 302 a-302N.

In an embodiment, a distance between the test point (or real time datapoint) to each entry in the p-table (e.g., pre-computed p-table) may bedetermined to identify a minimum value (e.g., closest entry) in thep-table to the test point that was collected. In some embodiments,multiple test points may be used. For example, a distance may bedetermined for each of a plurality of test points, from the respectivetest point to each entry in the p-table. The distance is the metric weare trying to optimize.

In some embodiments, the distance measurement module 350 may select adistance metric to perform the calculation. For example, and withoutlimitation, the distance measurement module 350 may use a Mahalanboisdistance or a standardized Euclidean distance to perform thecalculation. In some embodiments, the Mahalanbois distance or thestandardized Euclidean distance may be used to calculate the distancebetween each entry in the non-zero SCM (e.g., test points) and acorresponding entry in the p-table. The calculation is described belowin greater detail with respect to FIGS. 4A-4B. In an embodiment in whichthe Euclidean distance is used, an inverse covariance matrix may beprecomputed and applied to the p-table entries in order to reduce acomputation time of the MINDIST method.

The minimum distance module 355 may receive the calculated distancesfrom the distance measurement module 350. In an embodiment, the minimumdistance module 355 may identify a value from the calculated distancesbetween the test point (or multiple test points) and each entry in thep-table that is a minimum distance as compared with the other calculateddistance values. The value may be a minimum distance pointrepresentative of an angle of arrival of a signal on the plurality ofarray elements 302 a-302N.

In an embodiment, the minimum distance module 355 may output a signalindicating the minimum distance point, such as a DF output signal 360.The minimum distance may indicate the minimum value (e.g., closestentry) in the p-table to the test point that was collected. In someembodiments, the minimum distance point may correspond to the estimatedangle of arrival of the signal incident on one or more of the arrayelements 302 a-302N. Thus, the DF processor 330 may produce a DF outputsignal 360 representative of an estimated angle of arrival of a signalincident on one or more of the array elements 302 a-302N.

In an embodiment, DF processor 330 may be the same as or substantiallysimilar to DF module 130 described above with respect to FIG. 1.

Referring to FIG. 3B, an illustrative DF system 300′, which may besimilar to DF systems 100, 300 described above in conjunction with FIGS.1 and 3A, includes an array antenna 301′ having five subarrays 302a′-302 e′ and a receiver 307′ having four receiver channels 308 a′-308d′. In this illustrative embodiment, switch matrix 310′ selectivelycouples subarrays 302 a′-302 c′ to receiver channels 308 a′ and 308 b′while subarrays 302 d′ and 302 e′ are coupled directly to DF receiverchannels 308 c′ and 308 d′ respectively. Thus, not all of the subarrays302 a′-302 e′ are coupled to switch matrix 310.

The number of subarrays coupled to RF receiver channels 308 a′-308 d′through switch matrix 310′ versus being directly coupled to RF receiverchannels 308 a′-308 d′ (and subsequently to DF processor 330) can varybased at least in part upon a variety of factors, including but notlimited to, a particular application of DF system 100 and/or a number ofsubarrays in array 301′ as compared with a number of channels inreceiver 307′.

In this illustrative embodiment of FIG. 3B, the DF system 300′ includesfive subarrays formed from antenna elements which make up array 301′ andreceiver 307′ has only four channels. Three subarrays 302 a′, 302 b′,302 c′ and two receiver channels 308 a′ and 308 b′ are coupled to switchnetwork 310′, and two subarrays 302 d′ and 302 e′ are directly coupledto receiver channels 308 c′ and 308 d′. In other embodiments, however,it may be desirable or necessary for other combinations of subarraysand/or receiver channels to be coupled through a switch network.

In an alternate embodiment, however, it may be desirable or necessaryfor four subarrays and three receiver channels to be coupled to switchmatrix 310′ a single subarray directly coupled to a single RF receiverchannel. Alternatively still, in other embodiments two subarrays may beselectively coupled to a single receiver channel through switch matrix310′ and three subarrays may be coupled directly to individual receiverchannels. Other embodiments are also possible with systems having adifferent number of subarrays and receiver channels, (i.e. other thanfive subarrays and four receiver channels).

In the illustrative embodiment of FIG. 3B, switch matrix 310′ comprisestwo switch stages with a first stage comprising switches 304 a-304 ccoupled to second stage comprising switches 306 a-306 b. The switchesmay be provided as multi-pole switches and/or multi-throw switchesand/or multi-pole, multi-throw switches. For example, and as illustratedin FIG. 3B, switches 304 a-304 c in the first stage are provided assingle-pole, triple-throw switches having one input coupled to arespective one of the subarrays 302 a′-302 c′ and three outputs.Switches 306 a and 306 b are also provided as single-pole, triple-throwswitches. Three inputs of switches 306 a and 306 b are coupled torespective outputs of switches 304 a-304 c. Outputs of the second stageswitches are coupled to respective ones of receiver channels 308 a′-308b′.

Switches 304 a-304 c, 306 a, and 306 b are configured to couple signalsfrom selected ones of the subarrays 302 a′-302 c′ to receiver channels308 a′ and 308 b′. Since there are only two receiver channels coupled tothe output of switch 310′ (i.e. channels 308 a′ and 308 b′) switchmatrix 310′ operates to couple two of the three subarrays to the tworeceiver channels (i.e. two subarrays are active) and the third subarrayis inactive. In this illustrative embodiment, subarrays 302 a′ and 302b′ are coupled to respective ones of receiver channels 308 a′, 308 b′and subarray 308 c′ is made inactive by being coupled to a referencepotential (here ground) through a matched termination 305.

Thus, with such a switch configuration, a first group of subarrays, herefour subarrays 302 a′, 302 b′ 302 d′, 302 e′, may be coupled to the fourreceiver channels 308 a′-308 d′ during a dwell. During a next dwell,switches may be reconfigured such that a second group of subarrays (forexample subarrays 302 a′, 302 c′, 302 d′ and 302 e′) are coupled to thefour receiver channels 308 a′-308 d′. This process may be repeated foras many different dwells as there are different groups (or combinations)of subarrays.

Although FIG. 3B illustrates switches in the first stage as three-throwswitches having one input and three outputs and switches in the secondstage as having three inputs and one output, it should be appreciatedthat switch matrix 310′ may be designed using any of a variety ofdifferent types of multi-pole switches or multi-throw switches. A typeof switched used may depend, at least in part, upon the needs ofparticular application and the components of the DF system 300′ (e.g.,number of array elements, number of receiver channels). Thus, in someapplications, it may be possible or desirable or required to provideswitch matrix from a single multi-pole, multi-throw switch. For example,switch matrix 310′ may be provided as a two-pole, triple-throw switch(rather than being provided from a plurality of single-pole,triple-throw switches as illustrated in FIG. 3B).

FIGS. 4 and 4A are a series of flow diagrams showing illustrativeprocessing that can be implemented within a DF system such as DF system100, or 300 or 300′ described above in conjunction described above inconjunction with FIGS. 1, 3A, and 3B) and, more particularly, within aDF processor such as the DF processors 130 or 330 described above inconjunction with the illustrative systems of FIGS. 1, 3A, and 3B. Indescribing this processing, any of DF processors 130 or 330 may bereferred to but it should be understood that reference to one does notlimit the invention to that particular element. Rectangular elements(typified by element 402 in FIG. 4A), are denoted herein as “processingblocks,” and represent computer software instructions or groups ofinstructions. Diamond shaped elements (typified by element 408 in FIG.4A), are denoted herein as “decision blocks,” and represent computersoftware instructions, or groups of instructions, which affect theexecution of the computer software instructions represented by theprocessing blocks. Alternatively, the processing and decision blocks mayrepresent steps or processes performed by functionally equivalentcircuits such as a digital signal processor circuit or an applicationspecific integrated circuit (ASIC). The flow diagrams do not depict thesyntax of any particular programming language. Rather, the flow diagramsillustrate the functional information one of ordinary skill in the artrequires to fabricate circuits or to generate computer software toperform the processing required of the particular apparatus. It shouldbe noted that many routine program elements, such as initialization ofloops and variables and the use of temporary variables are not shown. Itwill be appreciated by those of ordinary skill in the art that unlessotherwise indicated, the particular sequence of blocks described isillustrative only and can be varied without departing from the spirit ofthe concepts, structures, and techniques described. Thus, unlessotherwise stated the blocks described below are unordered meaning that,when possible, the functions represented by the blocks can be performedin any convenient or desirable order.

Turning now to FIGS. 4A and 4B, an illustrative method 400 forperforming direction finding using a plurality of dwell spatial SCMs andan aggregate covariance matrix begins in processing block 402, in whicha first group of subarrays from a plurality of groups of subarrays maybe selected. RF signals may be received via the selected group ofsubarrays from one or a variety of different RF sources. For example,and as noted above, in some embodiments, the RF signals may correspondto a type of emergency beacon signal used in a variety of differentapplications, including but not limited to, airborne or ground-basedsearch and rescue applications.

At processing block 404, during a first dwell time, data is collectedvia the selected first group of subarrays. That is, signals received bythe first group of subarrays are concurrently coupled to a like numberof receiver channels (i.e. one receiver channel for each activesubarray) which process the signals and provide data from each subarrayto a DF processor (e.g. DF processor 330 in FIG. 3B).

The data from a signal received via a subarray may be captured at aninstantaneous point in time (e.g., a snapshot during each dwell time) orover a predetermined time period. Data is captured at a different one ofa plurality of dwell times for each group of subarrays. Thus, each groupof subarrays captures data at different dwell times (e.g., differentsnapshots). In some embodiments, the data may be captured atpredetermined dwell times.

Processing proceeds to processing block 406 where the data may berepresented as complex values (e.g., I/Q data) stored in a dwell spatialSCM for the first group of subarrays. The complex values may berepresentative of some, or all of an angle of arrival, amplitude, phase,and/or a polarization of the received signal. The data may be stored andfurther processed or analyzed in complex form.

In some embodiments, multiple samples may be taken of a single signal.In other embodiments, multiple samples may be taken of a plurality ofdifferent signals. In one embodiment, the data may correspond todifferent signals. It should be appreciated, that the number of datasamples taken may vary depending upon a variety of factors, includingbut not limited to, a number of array elements, a number of subarraysused and the requirements of a particular application. In someembodiments, the number of data samples taken may be based, at least inpart, upon a signal-to-noise ratio of the received RF signal. Forexample, for RF signals having a low signal-to-noise ratio it may bedesirable to collect more data samples to increase an accuracy of the DFprocessing technique as compared to processing signals having a highsignal to noise ratio.

At block 406, a dwell spatial SCM is generated using signals receivedduring the first dwell by the first group of subarrays. The dwell SCMmay be formed, for example, by a DF processor which may be the same asor similar to the DF processor 330 described in conjunction with FIGS.3A and 3B. In one embodiment, the dwell SCMs may be formed by the SCMmodule 335 of DF processor 330. The dwell SCMs may include data sampledat antenna phase centers of array elements in a respective group ofsubarrays during the respective dwell time. The data may be compared toother data from the respective group of subarrays. In an embodiment, thesize of the dwell SCM may correspond to the number of total groups ofsubarrays and thus include entries for both active array elements andinactive array elements. However, no data is received from the inactivesubarrays and thus a value of zero is entered in the respective matrixentries for the inactive subarrays. A sample dwell SCM is providedbelow:

${Sxx} = \begin{bmatrix}{S\; 11} & {S\; 12} & {S\; 13} & \ldots & {S\; 1N} \\{S\; 21} & {S\; 22} & {S\; 23} & \ldots & {S\; 2N} \\{S\; 31} & {S\; 32} & {S\; 33} & \ldots & {S\; 3N} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{S\; N\; 1} & {S\; N\; 2} & {{S\; N\; 3}\;} & \ldots & {SNN}\end{bmatrix}$

Where Sxy represents a comparison of a data sample taken at subarray xto a data sample taken at subarray y. In an embodiment, the sample dwellSCM may be the same as or substantially similar to Tables 1-3 describedabove in conjunction with FIGS. 3A-3B. The dwell SCM the matrix valuesare complex values (e.g., an amplitude and phase which may be expressedas A₁∠θ₁). In the dwell SCM above, values in a first column of the dwellSCM represent a difference between a sample of data taken at a firstsubarray to the samples taken at each of the other subarrays. Valuesalong the main diagonal of the dwell SCM represent the differencebetween two data samples taken at the same subarray. Hence, the valuesof the main diagonal are ideally equal to zero. An N^(th) column of thedwell SCM has values corresponding to the difference between a datasample taken at an N^(th) subarray to the data sample taken at each ofthe other subarrays in the group of subarrays. Thus, it should beappreciated that the dwell SCM may typically include a number of rowsand/or columns corresponding to the number of rows and/or columns in thegroup of subarrays. In some embodiments, the number of rows and/orcolumns in the dwell SCM may be based at least on the total number ofsubarrays in the group of subarrays (i.e. both active array elements andinactive elements).

At decision block 408, a decision is made as to whether data is to becollected through additional groups of subarrays. In an embodiment, thenumber of groups of subarrays used in a particular application can varyand may be based, at least in part, upon a total a number of elements inthe array antenna, a number of receiver channels, a desired gain patternof a DF system and/or a field of view of a DF system. As noted above,each different group of subarrays receives signals during a differentdwell. Thus, decision block 408 and processing blocks 410, 412 implementa loop during which data is collected and an SCM is formed for eachgroup of subarrays during the different dwells.

In an embodiment, method 400 may include a feedback mechanism tocontinually collect data from different groups of subarrays until adesired number of groups of subarrays have been sampled. For example, ifthe response to the decision block 408 is yes, then data is to becollected at more groups of subarrays and processing flows to processingblock 410, to select a next, different group of subarrays from theplurality of subarrays with which to collect data.

Next, at processing block 412, during a next dwell time, data may becollected via the newly selected group of subarrays. Processing whichoccurs at processing block 412 may be the same as or substantiallysimilar to processing which occurs at processing block 404, however thedata collected is from a different group of subarrays at a differentdwell time than in processing block 404.

Once data is collected in processing block 412, processing flows back toblock 406 to generate a dwell spatial SCM for the most recently selectedgroup of subarrays. Thus, method 400 may continually loop through blocks406, 408, 410, 412 until the desired number of groups of subarrays havebeen sampled. Once, the predetermined number of groups of subarrays havebeen sampled, the response to decision block 408 is no and processingflows to processing block 414.

At processing block 414, the dwell SCMs corresponding to each of thegroups of subarrays are combined to form an aggregate covariance matrix.In an embodiment, the SCM module 335 of DF processor 330 may combineeach of the dwell SCMs to generate the aggregate covariance matrix. Thedwell SCMs can be combined in complex form (e.g., quadrature space) toform the aggregate covariance matrix (i.e. the complex values in thedwell SCMs are combined in complex form to generate the aggregatecovariance matrix). The aggregate covariance matrix may be the same asor substantially similar to aggregate covariance matrix described abovein Table 4 in conjunction with FIGS. 3A-3B.

In one embodiment, a MINDIST technique is applied to data stored in theaggregate covariance matrix. A MINDIST technique is described in detailin co-pending application Ser. No. 15/260,508, filed on even dateherewith entitled “Systems And Methods For Direction Finding Based OnMinimum Distance Search To Principal Components,” which application isassigned to the assignee of the present application. In an embodiment,the MINDIST technique may include the processing illustrated inprocessing blocks 416-424.

In processing block 416, in accordance with a MINDIST technique, anangle SCM may be generated using the aggregate covariance matrix. Theangle SCM may be generated based upon phase values of elements in theaggregate covariance matrix. In an embodiment, the angle SCM may begenerated by the DF module 130 of FIG. 1 or the DF processor 330 ofFIGS. 3A-3B. For example, in one embodiment, the angle SCM may be formedby the SCM module 335 of DF processor 330. The angle SCM may be the sameas or substantially similar to angle SCM provided in Table 5 describedabove with respect to FIGS. 3A-3B.

In an embodiment, the angle SCM includes angle measurements for eachentry in the aggregate covariance matrix. The angle SCM may representangle measurements for the combination of array elements in each of thegroups of subarrays sampled. The angle measurements may be used todetermine or otherwise identify a phase difference between each of theentries in the aggregate covariance matrix or angle SCM. In anembodiment, the value of each entry in the angle SCM may represent acomparison of the data samples taken at the phase centers of each of thearray elements used in the combination of subarrays. In someembodiments, an angle value of the entries represents a phase differencebetween the array elements represented by the combination of groups ofsubarrays. For example, an entry corresponding to a sample comparisonbetween a first and second array element has an angle corresponding tothe phase difference between the first and second array element. Forexample, one embodiment of an angle SCM is provided below:

${\angle \; {Sxy}} = \begin{bmatrix}0 & {- {\Delta\Phi 12}} & {- {\Delta\Phi 13}} & \ldots & {- {\Delta\Phi 1N}} \\{\Delta\Phi 21} & 0 & {- {\Delta\Phi 23}} & \ldots & {- {\Delta\Phi 2N}} \\{\Delta\Phi 31} & {\Delta\Phi 31} & 0 & \ldots & {- {\Delta\Phi 3N}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{{\Delta\Phi N}\; 1} & {{\Delta\Phi N}\; 2} & {{\Delta\Phi N}\; 3} & \ldots & 0\end{bmatrix}$

Where ∠Sxx represents an angle defined by the comparison of a phasemeasurement at array element x to a phase measurement at array elementy. Thus, Δφ21, corresponds to a phase difference between a first and asecond element. In an embodiment, the angle SCM in Table 7 may be thesame as or substantially similar to angle SCM provided in Table 5described above with respect to FIGS. 3A-3B.

Still referring to block 416, in an embodiment, each non-zero element ofthe angle SCM may be identified. In an embodiment, each non-zero elementof the angle SCM may be identified by the DF module 130 of FIG. 1 or theDF processor 330 of FIGS. 3A-3B. For example, in one embodiment, eachnon-zero element of the angle SCM may be identified by the SCM module335 of DF processor 330.

Referring to the angle SCM above, it should be noted that the valuesalong the main matrix diagonal correspond to a phase difference takenbetween the phase of a single subarray and thus are zero.

It should also be noted that the angle measurement between a first andsecond array element may be included twice in the table (e.g., in thefirst column, Δφ21, and in the second column, −Δφ12). That is,Δφmn=−Δφnm. As Δφmn=−Δφnm, there is some redundancy in some of the phasedifferences and thus only one of these values may be needed forcomputations. Therefore, in some embodiments, only a portion of thenon-zero entries need be extracted from the angle SCM. Thus, a reductionin the number of array elements to be analyzed can be realized to reducean overall computation time of the MINDIST technique by extracting onlya portion of the non-zero entries from the angle SCM.

At block 418, a principal component table (p-table) may be computedusing angle and frequency measurements for one or more principalcomponents. In some embodiments, the p-table may be pre-computed. In anembodiment, the p-table may be generated by the DF module 130 of FIG. 1or the DF processor 330 of FIGS. 3A-3B. For example, in one embodiment,the p-table may be generated by the p-tables module 345 of DF processor330.

In an embodiment, the frequency and angle components of the p-table maybe precomputed in tables and can be extracted to the p-table. Forexample, the frequency and angle components can be pre-computed based onpreviously collected data and/or using estimated data. In an embodiment,the estimated data may be based on a known phase center location for arespective array element or data measured in a lab setting using, forexample, a computer model of the array element (or array antenna). Insome embodiments, the frequency and angle components may be computedprior to the one or more array elements receiving a signal or prior to asample of data being taken at the respective array elements. Thus, thefrequency and angle components may be extracted from the precomputedtables during execution of the MINDIST technique. In an embodiment, thetables may be look-up tables (or indexes) and the corresponding vectordata may be extracted after the principal components have beenidentified.

In other embodiments, the frequency and angle components may be computedsimultaneously or substantially simultaneously as the one or more arrayelements receiving signals or samples of data being taken at one or moreof the array elements.

In an embodiment, the frequency and angle components may be computed fora desired range. For example, a table may be generated by performing aprincipal components analysis for all angles within a desired rangerelative to predetermined azimuth range and a predetermined elevationrange. For example, and referring to equation 1 below:

$\begin{matrix}{\forall{\left( {\theta,\phi} \right) \in {\left\lbrack {{- \frac{\pi}{2}},\frac{\pi}{2}} \right\rbrack {X\left\lbrack {{- \frac{\pi}{2}},\frac{\pi}{2}} \right\rbrack}}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

As illustrated in Eq. 1, using a range defined by the azimuth range of

$\left\lbrack {{- \frac{\pi}{2}},\frac{\pi}{2}} \right\rbrack$

and the elevation range of

$\left\lbrack {{- \frac{\pi}{2}},\frac{\pi}{2}} \right\rbrack,$

frequency and angle components may be computed using an array manifoldvector. In an embodiment, the array manifold vector corresponds to thearray of array elements being analyzed. Thus, the array manifold vectorν(f, θ, φ), may be represented by equation 2 below:

$\begin{matrix}{{\overset{\_}{v}\left( {f,\theta,\phi} \right)} = \left\lbrack {e^{j\frac{2\pi \; f}{c_{light}}P_{{el}\; 1}^{- T}{\overset{\_}{u}{({\theta,\phi})}}},{e^{j\frac{2\pi \; f}{c_{light}}P_{{el}\; 2}^{- T}{\overset{\_}{u}{({\theta,\phi})}}}\mspace{14mu} \ldots \mspace{14mu} e^{j\frac{2\pi \; f}{c_{light}}P_{elk}^{- T}{\overset{\_}{u}{({\theta,\phi})}}}},} \right\rbrack^{T}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

in which:C_(light)=speed of lightP=Element positionT=vector Transpose operatorel,=the element indexū=normalized line of sight direction vector as a function of (θ, φ))θ=azimuth angleφ=elevation angle

The frequency and angle components may be computed for each of the arrayelements over a desired frequency range using the array manifold vector.In an embodiment, vector data may be generated for each of the arrayelements. In an embodiment, the vector data may be generated by the DFmodule 130 of FIG. 1 or the DF processor 330 of FIGS. 3A-3B. Forexample, in one embodiment, the vector data may be generated by thep-tables module 345 of DF processor 330.

In an embodiment, a vector data table may be generated by comparing thevector data from each of the array elements. For example, one embodimentof a vector data table is illustrated below:

${Svv} = \begin{bmatrix}{v_{1}v_{1}^{*}} & {v_{1}v_{2}^{*}} & {v_{1}v_{3}^{*}} & \ldots & {v_{1}v_{N}^{*}} \\{v_{2}v_{1}^{*}} & {v_{2}v_{2}^{*}} & {v_{2}v_{3}^{*}} & \ldots & {v_{2}v_{N}^{*}} \\{v_{3}v_{1}^{*}} & {v_{3}v_{2}^{*}} & {v_{3}v_{3}^{*}} & \ldots & {v_{3}v_{N}^{*}} \\\vdots & \vdots & \vdots & \ddots & \vdots \\{v_{N}v_{1}^{*}} & {v_{N}v_{2}^{*}} & {v_{N}v_{3}^{*}} & \ldots & {v_{N}v_{N}^{*}}\end{bmatrix}$

Where ν_(x) represents the vector data for array element x and ν_(x)*represents the complex conjugate of the vector data for array element x(i.e., ν₂ν₁=Δφ₂₁(f,θ, φ)). In an embodiment, to generate the vector datatable, the resulting vector data for each array element may bemultiplied by its complex conjugate and/or the complex conjugate ofvector data from another array element.

In some embodiments, the vector data table can be precomputed and thusused as a look-up table to pull specific entries corresponding to anextracted principal component. In an embodiment, the angle SCM (Table 7)may be used to identify the appropriate entries in the vector data table(Table 8) to be extracted. For example, the non-zero elements in theangle SCM correspond to the extracted principal components and in someembodiments, the entries in the vector table data corresponding to theextracted principal components may be extracted to generate a p-table.Thus, the number of entries in the p-table may correspond to the numberof extracted principal components (e.g., 1 entry per each extractedprincipal component) and the extracted entries from the vector datatable.

The p-table may sort each of the SCM elements having a non-zero angle bytheir respective vector data (e.g., by frequency and angle data). Thus,a final p-table may include the extracted principal component datasorted by frequency and angle. One embodiment of a p-table is providedin Table 9 below:

${\overset{\_}{p}\left( {f,\theta,\phi} \right)} = \begin{bmatrix}{{\Delta\Phi}_{21}\left( {f,\theta,\phi} \right)} \\{{\Delta\Phi}_{31}\left( {f,\theta,\phi} \right)} \\\vdots \\{{\Delta\Phi}_{M\; 1}\left( {f,\theta,\phi} \right)}\end{bmatrix}$

Where Δφ_(xy)(f,θ,φ) represents the vector data for an SCM elementcomparing array element x to array element y. For example, Δφ_(xy)represents a principal component angle value for SCM element comparingan X array element and a Y array element, f represents frequency, θrepresents a first angle value (e.g., azimuth angle) and φ represents asecond angle value (e.g., elevation angle). M represents the number ofentries in the p-table, which as indicated above, may correspond to thenumber of extracted principal components and the extracted entries fromthe vector data table. The p-table may include all of the vector datafor each of the extracted principal components.

In some embodiments, the number and/or size (i.e., number of entries inthe p-table) of p-tables generated may vary according to a particularapplication of the MINDIST technique. For example, in some embodiments,the p-table may be generated for a desired angular field of view (e.g.,±M° az, ±N° el). Thus, the number of entries in the p-table can varyaccording to the desired angular field of view. Further, the number ofentries in the p-table may correspond to the number of array elements inan antenna array and/or the number of principal components extractedfrom an SCM. For example, in some embodiments, for a K array elementarray, K′=(K²−K)/2 values or entries may be extracted from an SCM, whereK′ represents the number of principal components to be used (e.g.,principal components to be extracted) for a particular application ofthe MINDIST technique.

In some embodiments, a p-table may be broken up into N different (θ,φ)subspaces. Each of the subspaces may be processed independent of eachother or two or more of the N different (θ,φ) subspaces may be processedtogether. In an embodiment, each of the N different (θ,φ) subspaces maybe processed on different systems (e.g., N different processors), andmay be processed in simultaneously or substantially simultaneously.Thus, the computation time may be reduced by 1/N. The results from eachsystem, each of the N different processors, may be compared to identifya minimum distance point.

In some embodiments, the one or more tables (e.g., non-zero SCM elementtable, vector data table) may be computed prior to one or more arrayelements receiving a signal and/or a sample of data being taken of asignal received at one or more array elements. For example, the tablesmay be pre-computed using previously collected data for an array and/orestimated data for an array (e.g., estimated snapshots at one or morearray elements). In other embodiments, tables (e.g., non-zero SCMelement table, vector data table) may be generated simultaneously to oneor more array elements receiving a signal and/or a sample of data beingtaken of a signal received at one or more array elements.

At block 420, principal components may be extracted from the aggregatedcovariance matrix or the angle SCM to form a test point. In anembodiment, the principal components may be extracted by the DF module130 of FIG. 1 or the DF processor 330 of FIGS. 3A-3B. For example, inone embodiment, the principal components may be extracted by theprincipal components module 340 of DF processor 330.

In an embodiment, the principal components in the p-table may correspondto desired values from the aggregate covariance matrix or the angle SCM.The principal components may be sorted in the p-table by theirrespective frequency, angle and/or phase components. Thus, thecorresponding principal components can be identified in the aggregatecovariance matrix or the angle SCM using their respective angle andfrequency measurements.

In an embodiment, the principal components may correspond to entries inthe angle SCM having a non-zero angle. For example, and referring backto Table 7, phase difference measurements that are greater than or lessthan zero (i.e., not equal to zero) may be identified in the angle SCM.The phase difference measurements that are greater than or less thanzero may be extracted from the angle SCM and represent principalcomponents.

In one embodiment, one-half of the non-zero entries may be extractedfrom the angle SCM. In other embodiments, the number of principalcomponents extracted may vary depending upon a variety of factorsincluding, but not limited to a frequency of the signal(s) beinganalyzed. For example, in one embodiment, as the frequency increases,the variance (e.g., power) may be distributed across the principalcomponents. In some embodiments, a signal having a frequency can have asmaller wavelength and therefore more information may be contained inmore of the antenna phase centers. Alternatively, in other embodiments,a signal having a lower frequency may have a longer wavelengths andfewer phase centers may be needed to perform the MINDIST technique.Thus, more principal components may be needed to compute a minimumdistance point at a higher frequency than at a lower frequency.

At block 422, a distance may be determined between a test point and eachvalue in the principal component table. In an embodiment, the distancemeasurement may be determined by the DF module 130 of FIG. 1 or the DFprocessor 330 of FIGS. 3A-3B. For example, in one embodiment, thedistance measurement may be determined by the distance measurementmodule 350 of DF processor 330.

The test point may refer to data collected (e.g., data snapshot) takenduring an operation of a DF system (e.g., radar system, antenna system).The test point can be selected from the real-time data collected fromthe snapshots at one or more of array elements 102 a-102 n of FIG. 1,one or more of the array elements 302 a-302N of FIG. 3A, and/or 302a′-302 e′ of FIG. 3B. For example, the test point may be formed byextracting principal components as complex phases from the aggregatecovariance matrix or the angle SCM generated by SCM module 335. Thus,the test point may correspond to data currently received from arrayelements 302 a-302N.

In some embodiments, multiple test points may be used. The data from thetest point may be measured against the values in the p-table to identifya minimum distance point (or a closest entry in the p-table to the testpoint collected). In some embodiments, multiple test points may be used.For example, in one embodiment, a distance may be calculated betweeneach test point and each entry in the p-table. In an embodiment, a testpoint may be arbitrarily chosen from a plurality of snapshots (e.g.,real time data points) collected during operation. In other embodiments,the test point may be predetermined.

In an embodiment, for each of the entries (values) in the p-tables, adistance may be calculated from the respective entry to the test point.In some embodiments, a distance metric may be selected to perform thecalculation. For example, and without limitation, a Mahalanbois distance(equation 3 below) or a Euclidean distance (equation 4 below) to performthe calculation.

$\begin{matrix}{D_{mahal} = \sqrt{\left( {\overset{\_}{x} - \overset{\_}{p}} \right)^{T}{S^{- 1}\left( {\overset{\_}{x} - \overset{\_}{p}} \right)}}} & {{Eq}.\mspace{14mu} 3} \\{D_{seuc} = \sqrt{{\left( {\overset{\_}{x} - \overset{\_}{p}} \right)^{T}\left\lbrack \begin{pmatrix}\frac{1}{\sigma_{1}^{2}} & \ldots & 0 \\\vdots & \ddots & \vdots \\0 & \ldots & \frac{1}{\sigma_{N}^{2}}\end{pmatrix} \right\rbrack}\left( {\overset{\_}{x} - \overset{\_}{p}} \right)}} & {{Eq}.\mspace{14mu} 4}\end{matrix}$

where x represents the Xth p-table entry data and p represents the testpoint data. In an embodiment in which the Euclidean distance is used, aninverse covariance matrix may be formed from the distribution of datapoints in the p-table for each principal component. The inversecovariance matrix can be applied in the distance measurement to computethe Euclidean distance between the test point and each of the p-tableentries. In an embodiment, the inverse covariance matrix may beprecomputed and applied to the p-table entries in order to reduce acomputation time of the MINDIST technique.

At block 424, a minimum distance point corresponding to a direction ofthe received signal may be identified. In an embodiment, the minimumdistance point may be determined by the DF module 130 of FIG. 1 or theDF processor 330 of FIGS. 3A-3B. For example, in one embodiment, theminimum distance point may be determined by the minimum distance module355 of DF processor 330.

In an embodiment, each of the measured distances for each of the entriesin the p-table may be compared to identify a minimum distance point. Forexample, in one embodiment, equation 5, as provided below, may be usedto solve for the minimum distance point.

({circumflex over (θ)},{circumflex over (φ)})=arg_((θ,φ))min D(θ,φ)  Eq.5

A point having the minimum distance to the test point, as compared withthe other entries in the p-table may be identified. The value may be aminimum distance point representative of an angle of arrival of a signalon the one or more array elements.

In some embodiments, an estimation of the expected minimum point for aspecific frequency may be generated. For example, in some embodiments,an expected minimum point may be estimated using previously collecteddata and/or estimations of array properties. Each of the entries in thep-table may be compared to the estimation to identify the entry in thep-table that is closest the estimation for the frequency.

In an embodiment, an output signal may be generated indicating theminimum distance point, such as a DF output signal. In an embodiment,the output signal may be generated by the DF module 130 of FIG. 1 or theDF processor 330 of FIGS. 3A-3B. The DF output signal may be anestimated angle of arrival of a signal incident one or more of the arrayelements. In some embodiments, the estimated angle of arrival of thesignal may be a two-dimensional (2D) estimation.

Referring now to FIG. 5, a computer 500 includes a processor 502, avolatile memory 504, a non-volatile memory 506 (e.g., hard disk), agraphical user interface (GUI) 508 (e.g., a mouse, a keyboard, adisplay, for example) and a computer disk 520. The non-volatile memory506 stores computer instructions 512, an operating system 516 and data518. In an embodiment, the data 518 may include data collectedcorresponding to signals received at one or more array elements. Thedata may include complex I/Q data representing the signal. For example,in some embodiments, the data may include complex voltage signalsrepresentative of angle, amplitude, phase, and/or a polarization of thesignal. In embodiment, the data may include an angle measurement of thesignal relative to the phase center of the respective array element thatreceived the signal. In an embodiment, the data may be a snapshot of thesignal at a predetermined period of time or over a predetermined timeperiod.

In some embodiments, non-volatile memory 506 includes a look-up tablethat stores and organizes data corresponding to the data collected, aswell as any tables (e.g., p-tables, dwell SCMs, aggregate SCM, angleSCM, SCM phase different matrices, vector data tables) or matricesgenerated using the samples of data. In one example, the computerinstructions 512 are executed by the processor 502 out of volatilememory 504 to perform all or part of the method (or process) 400 ofFIGS. 4A and 4B.

In an embodiment, computer 500 may be the same as or substantiallysimilar to each of the components of the DF module 130 and/or DFprocessor 330, for example, the spatial SCM module 335, principalcomponents module 340, distance measurement module 350, minimum distancemodule 355 and p-table module 345. Computer 500 may perform all of thesame functions and be configured to receive and generate the same dataas each of the components of the DF module 130 and/or DF processor 330as described herein, such as the spatial sample covariance matrix (SCM)module 335, principal components module 340, distance measurement module350, minimum distance module 355 and p-table module 345. For example,computer 500 may be configured to perform real time direction findingdeterminations, capture data corresponding to signals incident on or oneor more array elements and generate tables and/or matrices (e.g.,p-tables, dwell SCMs, aggregate SCM, angle SCM, SCM phase differentmatrices, vector data tables) to identify a direction of arrival of asignal.

Method 400 is not limited to use with the hardware and software of FIG.5; they may find applicability in any computing or processingenvironment and with any type of machine or set of machines that iscapable of running a computer program. Method 400 may be implemented inhardware, software, or a combination of the two. Method 400 may beimplemented in computer programs executed on programmablecomputers/machines that each includes a processor, a storage medium orother article of manufacture that is readable by the processor(including volatile and non-volatile memory and/or storage elements), atleast one input device, and one or more output devices. Program code maybe applied to data entered using an input device to perform method 400and to generate output information.

The system may be implemented, at least in part, via a computer programproduct, (e.g., in a machine-readable storage device), for execution by,or to control the operation of, data processing apparatus (e.g., aprogrammable processor, a computer, or multiple computers)). Each suchprogram may be implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms may be implemented in assembly or machine language. Thelanguage may be a compiled or an interpreted language and it may bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. Alternatively, the system may be implemented, at least inpart, as firmware.

A computer program may be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network. A computer program may bestored on a storage medium or device (e.g., CD-ROM, hard disk, ormagnetic diskette) that is readable by a general or special purposeprogrammable computer for configuring and operating the computer whenthe storage medium or device is read by the computer to perform method400. Method 400 may also be implemented as a machine-readable storagemedium, configured with a computer program, where upon execution,instructions in the computer program cause the computer to operate inaccordance with method 400.

Method 400 may be performed by one or more programmable processorsexecuting one or more computer programs to perform the functions of thesystem. All or part of the system may be implemented as, special purposelogic circuitry (e.g., an FPGA (field programmable gate array) and/or anASIC (application-specific integrated circuit)).

A number of embodiments of the disclosure have been described.Nevertheless, it will be understood that various modifications may bemade without departing from the spirit and scope of the disclosure.Elements of different embodiments described herein may be combined toform other embodiments not specifically set forth above. Otherembodiments not specifically described herein are also within the scopeof the following claims.

What is claimed:
 1. In a direction finding system having an array antenna having a first plurality of subarrays and a second, different plurality of receiver channels, a method for direction finding comprising: selecting groups of subarrays from among the plurality of groups of subarrays with which to receive signals; configuring each of the selected groups of subarrays to receive signals during a corresponding one of a plurality of different dwell times; at each of the plurality of dwell times, simultaneously coupling respective ones of a selected group of subarrays to respective ones of the plurality of receiver channels; generating a plurality of dwell spatial sample covariance matrices (SCMs) using data from the receiver channels corresponding to the groups of subarrays; and combining the plurality of dwell spatial SCMs in complex form to generate an aggregate covariance matrix; and using values from the aggregate covariance matrix to generate a DF output signal indicative of a direction of a signal received by at least some of the selected groups of subarrays.
 2. The method of claim 1 wherein simultaneously coupling respective ones of the selected groups of subarrays to the respective receiver channels at a different dwell time comprises operating a switch to couple at least some subarrays in the group of subarrays to a respective receiver channel.
 3. The method of claim 1 wherein using values from the aggregate covariance matrix further comprises: pre-computing a principal component table using angle and frequency measurement for one or more principal components; extracting the one or more principal components as complex phases from the aggregate covariance matrix to form a test point; determining a distance between the test point and each value in the pre-computed principal component table; and identifying a minimum distance point based on the determined distances between the test point and each value in the principal component table, wherein the minimum distance point corresponds to a direction of the received signals.
 4. The method of claim 1, further comprising generating one or more dwells using the data from the plurality of groups of subarrays, wherein each dwell corresponds to at least one dwell time of the plurality of different dwell times.
 5. The method of claim 1, further comprising: capturing data at a first subarray at a first dwell time; capturing data at a second subarray at a second dwell time; generating a dwell spatial SCM for each of the first subarray and the second subarray; and combining the dwell spatial SCMs in complex form to form the aggregate covariance matrix.
 6. The method of claim 1, further comprising providing received signals from a first group of subarrays to a switch matrix and providing received signals from a second group of subarrays directly to a direction finding module.
 7. The method of claim 1, wherein each of the data includes an angle measurement corresponding to the received data relative to a phase center of one or more of the plurality of array elements.
 8. The method of claim 7, wherein each of the dwell SCMs and the aggregate covariance matrix include angle measurements for the plurality of array elements.
 9. The method of claim 8, further comprising identifying a phase difference between each of the elements in the aggregate covariance matrix using the angle measurements.
 10. The method of claim 1, further comprising determining vector data for each of the plurality of array elements, wherein the vector data includes an angle and a frequency measurement.
 11. The method of claim 2, wherein the principal component table includes principal component data sorted by frequency measurements and angle measurements for each of the one or more principal components.
 12. A system comprising: a plurality of subarray, each of the subarrays having an output port; a radio frequency (RF) receiver having one or more receiver channels, wherein the number of subarrays is greater than the number receiver channels; and a switch network having a plurality of input ports with each input port coupled to an output of one or more respective ones of said plurality of subarrays and having output ports with each switch output coupled to an input of respective one of the one or more receiver channels, said switch network operable to switch between different groups of subarrays at different dwell times such that at least one group of subarrays is configured to provide signals to the receiver channels; a direction finding processor coupled to receive signals from said RF receiver during a selected one of a plurality of dwell times.
 13. The system of claim 12, wherein said receiver is configured to simultaneously provide signals to said DF processor of subarrays is active and at least one array element in each of the groups of subarrays is inactive.
 14. The system of claim 12, wherein the switch matrix comprises a plurality of stages and each of the plurality of stages includes two or more switches.
 15. The system of claim 12, wherein each of the plurality of subarrays is coupled to an input of said switch network.
 16. The system of claim 12, wherein at least one subarray of the group of subarrays is coupled directly to a receiver channel.
 17. The system of claim 12, wherein each of the plurality of subarrays comprise one or more antenna elements coupled directly to the direction finding module.
 18. The system of claim 12, wherein the direction finding module comprises: a spatial sample covariance matrix (SCM) module to receive the data and generate one or more dwell spatial sample covariance matrices (SCMs) and an aggregate covariance matrix using the data; a p-table module to generate a table having components as a function of frequency and angle measurements; a principal component module coupled to the SCM module and the p-table module, wherein the principal component module generates a principal component table having one or more principal components sorted by the frequency and angle measurements; a distance measurement module coupled to the principal component module, wherein the distance measurement module calculates a distance from a test point to each entry in the principal component table; and a minimum distance module coupled to the distance measurement module, wherein the minimum distance measurement module determines a minimum distance point based on the calculated distances from the test point to each entry in the principal component table. 